We map embeddings using a train seed dictionary (dictionary with word meanings) and state of the art supervised mapping method by [1]}, and calculate the accuracy of the mapping on the test dictionary. In short, the essence of this method is to find optimal orthogonal transforms for both embedding spaces to map them to a shared space based on a seed dictionary, plus some additional steps such as embeddings normalization. For each model—SG, FastText, and MorphGram, we mapped Russian and English embeddings trained using this model. We used the original implementationhttps://github.com/artetxem/vecmap for mapping (supervised option), and ground-truth train/test dictionaries provided by Facebook for their MUSEhttps://github.com/facebookresearch/MUSE library. We report 1-nn and 10-nn accuracy: whether the correct translation was found as a first nearest neighbor or among 10 nearest neighbors of a word in the mapped space. See the results in Table REF .
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